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**Pearson相关的统计效应与效力（Statistical effects and potency of Pearson correlation）**
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  *用r的平方解释相关关系强度：一个变量的方差中，由X和Y间的相关解释的方差的比例。比如：当r=0.7时，Y变异的一部分能由X推出，r2=0.49，即Y 49%的变异能够由X推出。
  *Pearson相关的效应：r=.10，小的效应；r=.30，中等效应；r=.50，大的效应。
  *Pearson相关统计效力的影响因素：①效应大小；②样本容量；③单尾/双尾。
  *相关描述两个变量之间的关系,但并不能解释变量相关的原因，即相关计算不能得到因果性推论。原因：在相关研究中, 研究者没有操纵一个 (或几个) 变量而保持其他变量不变。
* Strength of correlation explained in terms of r squared: The proportion of variance in a variable explained by the correlation between X and Y. For example, when r=0.7, a portion of the variation in Y can be derived from X, and r2=0.49, that is, 49% of the variation in Y can be derived from X.

*Pearson related effect: r=.10, small effect; r=.30, medium effect; r=.50, large effect.

* The influencing factors of Pearson correlation statistical efficacy: ① effect size; ② Sample size; ③ Single tail/double tail.

* Correlation describes the relationship between two variables, but does not explain why the variables are correlated, that is, correlation calculations do not yield causal inferences. Reason: In related studies, researchers did not manipulate one (or several) variables while keeping the others constant.